



'''simple docstring'''


import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs




class   __UpperCamelCase							(_UpperCAmelCase      ):







	def __init__(     self			,					_lowerCAmelCase			,					_lowerCAmelCase=None			,					_lowerCAmelCase=True			,					_lowerCAmelCase=None			,					**_lowerCAmelCase    )     ->     Any:
							'''simple docstring'''






							lowercase   =				parent
							lowercase   =				config_class
							lowercase   =				has_text_modality
							lowercase   =				kwargs
							lowercase   =				common_properties







	def 	_a (     self    )     ->     Dict:
							'''simple docstring'''






							lowercase   =				self.config_class(**self.inputs_dict    )
							lowercase   =				(
							    ['hidden_size', 'num_attention_heads', 'num_hidden_layers']
							    if self.common_properties is None
							    else self.common_properties
							)

							# Add common fields for text models
							if self.has_text_modality:
													common_properties.extend(["""vocab_size"""]    )

							# Test that config has the common properties as getters
							for prop in common_properties:
													self.parent.assertTrue(hasattr(_lowerCAmelCase			,					_lowerCAmelCase    )			,					msg=F"""`{prop}` does not exist"""    )

							# Test that config has the common properties as setter
							for idx, name in enumerate(_lowerCAmelCase    ):
													try:
																			setattr(_lowerCAmelCase			,					_lowerCAmelCase			,					_lowerCAmelCase    )
																			self.parent.assertEqual(
																			    getattr(_lowerCAmelCase			,					_lowerCAmelCase    )			,					_lowerCAmelCase			,					msg=F"""`{name} value {idx} expected, but was {getattr(_lowerCAmelCase			,					_lowerCAmelCase    )}"""    )
													except NotImplementedError:
																			# Some models might not be able to implement setters for common_properties
																			# In that case, a NotImplementedError is raised
																			pass

        # Test if config class can be called with Config(prop_name=..)
							for idx, name in enumerate(_lowerCAmelCase    ):
													try:
																			lowercase   =				self.config_class(**{name: idx}    )
																			self.parent.assertEqual(
																			    getattr(_lowerCAmelCase			,					_lowerCAmelCase    )			,					_lowerCAmelCase			,					msg=F"""`{name} value {idx} expected, but was {getattr(_lowerCAmelCase			,					_lowerCAmelCase    )}"""    )
													except NotImplementedError:
																			# Some models might not be able to implement setters for common_properties
																			# In that case, a NotImplementedError is raised
																			pass







	def 	_a (     self    )     ->     List[Any]:
							'''simple docstring'''






							lowercase   =				self.config_class(**self.inputs_dict    )
							lowercase   =				json.loads(config.to_json_string()    )
							for key, value in self.inputs_dict.items():
													self.parent.assertEqual(obj[key]			,					_lowerCAmelCase    )







	def 	_a (     self    )     ->     List[Any]:
							'''simple docstring'''






							lowercase   =				self.config_class(**self.inputs_dict    )

							with tempfile.TemporaryDirectory() as tmpdirname:
													lowercase   =				os.path.join(_lowerCAmelCase			,					"""config.json"""    )
													config_first.to_json_file(_lowerCAmelCase    )
													lowercase   =				self.config_class.from_json_file(_lowerCAmelCase    )

							self.parent.assertEqual(config_second.to_dict()			,					config_first.to_dict()    )







	def 	_a (     self    )     ->     Optional[int]:
							'''simple docstring'''






							lowercase   =				self.config_class(**self.inputs_dict    )

							with tempfile.TemporaryDirectory() as tmpdirname:
													config_first.save_pretrained(_lowerCAmelCase    )
													lowercase   =				self.config_class.from_pretrained(_lowerCAmelCase    )

							self.parent.assertEqual(config_second.to_dict()			,					config_first.to_dict()    )







	def 	_a (     self    )     ->     List[Any]:
							'''simple docstring'''






							lowercase   =				self.config_class(**self.inputs_dict    )

							lowercase   =				'test'
							with tempfile.TemporaryDirectory() as tmpdirname:
													lowercase   =				os.path.join(_lowerCAmelCase			,					_lowerCAmelCase    )
													config_first.save_pretrained(_lowerCAmelCase    )
													lowercase   =				self.config_class.from_pretrained(_lowerCAmelCase			,					subfolder=_lowerCAmelCase    )

							self.parent.assertEqual(config_second.to_dict()			,					config_first.to_dict()    )







	def 	_a (     self    )     ->     Any:
							'''simple docstring'''






							lowercase   =				self.config_class(**self.inputs_dict			,					num_labels=5    )
							self.parent.assertEqual(len(config.idalabel    )			,					5    )
							self.parent.assertEqual(len(config.labelaid    )			,					5    )

							lowercase   =				3
							self.parent.assertEqual(len(config.idalabel    )			,					3    )
							self.parent.assertEqual(len(config.labelaid    )			,					3    )







	def 	_a (     self    )     ->     List[str]:
							'''simple docstring'''






							if self.config_class.is_composition:
													return
							lowercase   =				self.config_class()
							self.parent.assertIsNotNone(_lowerCAmelCase    )







	def 	_a (     self    )     ->     str:
							'''simple docstring'''






							lowercase   =				copy.deepcopy(_lowerCAmelCase    )
							lowercase   =				self.config_class(**_lowerCAmelCase    )
							lowercase   =				[]
							for key, value in config_common_kwargs.items():
													if key == "torch_dtype":
																			if not is_torch_available():
																									continue
																			else:
																									import torch

																									if config.torch_dtype != torch.floataa:
																															wrong_values.append(("""torch_dtype""", config.torch_dtype, torch.floataa)    )
													elif getattr(_lowerCAmelCase			,					_lowerCAmelCase    ) != value:
																			wrong_values.append((key, getattr(_lowerCAmelCase			,					_lowerCAmelCase    ), value)    )

							if len(_lowerCAmelCase    ) > 0:
													lowercase   =				'\n'.join([F"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values]    )
													raise ValueError(F"""The following keys were not properly set in the config:\n{errors}"""    )







	def 	_a (     self    )     ->     Tuple:
							'''simple docstring'''






							self.create_and_test_config_common_properties()
							self.create_and_test_config_to_json_string()
							self.create_and_test_config_to_json_file()
							self.create_and_test_config_from_and_save_pretrained()
							self.create_and_test_config_from_and_save_pretrained_subfolder()
							self.create_and_test_config_with_num_labels()
							self.check_config_can_be_init_without_params()
							self.check_config_arguments_init()










"""simple docstring"""


import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


__SCREAMING_SNAKE_CASE   =   {
    'n_samples': 64,
    'horizon': 32,
    'num_inference_steps': 20,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   'hopper-medium-v2'
		__SCREAMING_SNAKE_CASE   =   gym.make(env_name)

		__SCREAMING_SNAKE_CASE   =   ValueGuidedRLPipeline.from_pretrained(
		    'bglick13/hopper-medium-v2-value-function-hor32',
		    env=env,
		)

		env.seed(0)
		__SCREAMING_SNAKE_CASE   =   env.reset()
		__SCREAMING_SNAKE_CASE   =   0
		__SCREAMING_SNAKE_CASE   =   0
		__SCREAMING_SNAKE_CASE   =   10_00
		__SCREAMING_SNAKE_CASE   =   [obs.copy()]
		try:
				for t in tqdm.tqdm(range(T)):
						# call the policy
						__SCREAMING_SNAKE_CASE   =   pipeline(obs, planning_horizon=32)

						# execute action in environment
						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE   =   env.step(denorm_actions)
						__SCREAMING_SNAKE_CASE   =   env.get_normalized_score(total_reward)

						# update return
						total_reward += reward
						total_score += score
						print(
						    F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
						    F""" {total_score}"""
						)

						# save observations for rendering
						rollout.append(next_observation.copy())

						__SCREAMING_SNAKE_CASE   =   next_observation
		except KeyboardInterrupt:
				pass

		print(F"""Total reward: {total_reward}""")